Search Results for author: Yanyan Xu

Found 12 papers, 6 papers with code

Human Mobility Prediction with Causal and Spatial-constrained Multi-task Network

1 code implementation12 Jun 2022 Zongyuan Huang, Shengyuan Xu, Menghan Wang, Hansi Wu, Yanyan Xu, Yaohui Jin

Next location prediction is one decisive task in individual human mobility modeling and is usually viewed as sequence modeling, solved with Markov or RNN-based methods.

Multi-Task Learning

SanMove: Next Location Recommendation via Self-Attention Network

no code implementations15 Dec 2021 Huifeng Li, Bin Wang, Sulei Zhu, Yanyan Xu

To address the above challenges, we propose a novel method named SanMove, a self-attention network based model, to predict the next location via capturing the long- and short-term mobility patterns of users.

PG$^2$Net: Personalized and Group Preferences Guided Network for Next Place Prediction

1 code implementation15 Oct 2021 Huifeng Li, Bin Wang, Fan Xia, Xi Zhai, Sulei Zhu, Yanyan Xu

Targeting this, we propose an end-to-end framework named personalized and group preference guided network (PG$^2$Net), considering the users' preferences to various places at both individual and collective levels.

Graph Embedding Management

Enhancing Unsupervised Anomaly Detection with Score-Guided Network

1 code implementation10 Sep 2021 Zongyuan Huang, Baohua Zhang, Guoqiang Hu, Longyuan Li, Yanyan Xu, Yaohui Jin

Anomaly detection plays a crucial role in various real-world applications, including healthcare and finance systems.

Unsupervised Anomaly Detection

An Empirical Study on End-to-End Singing Voice Synthesis with Encoder-Decoder Architectures

no code implementations6 Aug 2021 Dengfeng Ke, Yuxing Lu, Xudong Liu, Yanyan Xu, Jing Sun, Cheng-Hao Cai

With the rapid development of neural network architectures and speech processing models, singing voice synthesis with neural networks is becoming the cutting-edge technique of digital music production.

Singing Voice Synthesis

Speech Enhancement using Separable Polling Attention and Global Layer Normalization followed with PReLU

no code implementations6 May 2021 Dengfeng Ke, Jinsong Zhang, Yanlu Xie, Yanyan Xu, Binghuai Lin

With all these modifications, the size of the PHASEN model is shrunk from 33M parameters to 5M parameters, while the performance on VoiceBank+DEMAND is improved to the CSIG score of 4. 30, the PESQ score of 3. 07 and the COVL score of 3. 73.

Speech Enhancement

Dynamically Mitigating Data Discrepancy with Balanced Focal Loss for Replay Attack Detection

1 code implementation25 Jun 2020 Yongqiang Dou, Haocheng Yang, Maolin Yang, Yanyan Xu, Dengfeng Ke

Besides, in the experiments, we select three kinds of features that contain both magnitude-based and phase-based information to form complementary and informative features.

Speaker Verification

Travel Time Estimation without Road Networks: An Urban Morphological Layout Representation Approach

no code implementations8 Jul 2019 Wuwei Lan, Yanyan Xu, Bin Zhao

Travel time estimation is a crucial task for not only personal travel scheduling but also city planning.


Complementary Fusion of Multi-Features and Multi-Modalities in Sentiment Analysis

3 code implementations17 Apr 2019 Feiyang Chen, Ziqian Luo, Yanyan Xu, Dengfeng Ke

Therefore, in this paper, based on audio and text, we consider the task of multimodal sentiment analysis and propose a novel fusion strategy including both multi-feature fusion and multi-modality fusion to improve the accuracy of audio-text sentiment analysis.

Multimodal Emotion Recognition Multimodal Sentiment Analysis

Trainable back-propagated functional transfer matrices

1 code implementation28 Oct 2017 Cheng-Hao Cai, Yanyan Xu, Dengfeng Ke, Kaile Su, Jing Sun

In experiments, it is demonstrated that the revised rules can be used to train a range of functional connections: 20 different functions are applied to neural networks with up to 10 hidden layers, and most of them gain high test accuracies on the MNIST database.

Learning of Human-like Algebraic Reasoning Using Deep Feedforward Neural Networks

no code implementations25 Apr 2017 Cheng-Hao Cai, Dengfeng Ke, Yanyan Xu, Kaile Su

Briefly, in a reasoning system, a deep feedforward neural network is used to guide rewriting processes after learning from algebraic reasoning examples produced by humans.


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